There are inherent differences between AUROC and AUPRC. For anomaly detection tasks where class (anomaly or normal data points) distributions are heavily imbalances, the AUROC metric is less insensitive to false positives than the AUPRC metric. As discussed in [1]}, [2]}, the AUPRC score is more discriminative than AUROC score. Previous work may choose one of the metrics to evaluate the performance. To comprehensively evaluate the model's performance, we report both AUROC and AUPRC scores, but we mainly focus on analysis AUPRC in this paper.
